Fault detection and classification method using DWT and SVM in a power distribution network

Magagula, X. G.; Hamam, Yskandar; Jordaan, J. A.; Yusuff, Adedayo A.

doi:10.1109/powerafrica.2017.7991190

Cited by 29 publications

(13 citation statements)

References 8 publications

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“…The potency of the offered MMF-based feature extraction scheme is compared with the other existing techniques [20], [24], [34], [35] from the perspectives of group delay and data window size. The obtained results are revealed in Table 7.…”

Section: B Application To Emtp-generated Signalsmentioning

confidence: 99%

Mathematical Morphology-Based Feature-Extraction Technique for Detection and Classification of Faults on Power Transmission Line

Godse

Bhat

2020

IEEE Access

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The permanency of highly-reliable power supply is a core trait of an electric power transmission network. A transmission line is the main part of this network through which power is transmitted to the utility. These lines are often damaged by accidental breakdowns owing to different random origins. Hence, researchers are trying to detect and identify these failures at the earliest to avoid financial losses. This paper offers a new real-time fast mathematical morphology-based fault feature extraction scheme for detection and classification of transmission line faults. The morphological median filter is exploited to wrest unique fault features which are then fed as an input to a decision tree classifier to classify the fault type. The acquired graphical and numerical results of the extracted features affirm the potency of the offered scheme. The proposed scheme is verified for different fault cases simulated on high-voltage transmission line modelled using ATP/EMTP with varying system constraints. The performance of the stated technique is also validated for fault detection and classification on real-field transmission lines. The results state that the proposed method is capable of detecting and classifying the faults with adequate precision and reduced computational intricacy, in less than a quarter of a cycle.

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Section: B Application To Emtp-generated Signalsmentioning

confidence: 99%

Mathematical Morphology-Based Feature-Extraction Technique for Detection and Classification of Faults on Power Transmission Line

Godse

Bhat

2020

IEEE Access

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“…As the proposed technique avails a two-sample data window, the fault detection and classification is achieved in less than the quarter of the cycle. The potency of the offered combined OCMF-based feature extraction scheme is compared with the other existing techniques WT+SVM [15], PCA+SVM [19], determinant function combined with SVM (DF+SVM) [22] from the perspectives of group delay, data window size and CA. The obtained results are revealed in Table 7.…”

Section: Fault Typementioning

confidence: 99%

“…ML-based techniques i.e. artificial neural network (ANN), k-nearest neighbor (kNN), and support vector machine (SVM) combined with the abovementioned feature extraction techniques are used for detecting and classifying the faults [8][9][10][11][12][13][14][15][16][17][18][19][20][21]. ii) Does not need to assume the periodicity of the signal.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, SVM combined with abovementioned feature extraction techniques have been widely used for fault detection and classification on the transmission line. In [14][15][16][17], fault features extracted using WT are used as input to SVM for classifying faults on the transmission line. In [18], efficient and reliable detection of faults on the transmission line is achieved from the fault features extracted using ST analysis further SVM is deployed for identifying a fault type.…”

Section: Introductionmentioning

confidence: 99%

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Combined morphology and SVM-based fault feature extraction technique for detection and classification of transmission line faults

Godse¹,

Bhat²

2020

Turk J Elec Eng & Comp Sci

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A transmission line is the main commodity of power transmission network through which power is transmitted to the utility. These lines are often swayed by accidental breakdowns owing to different random origins. Hence, researchers try to detect and track down these failures at the earliest to avoid financial prejudice. This paper offers a new realtime mathematical morphology based approach for fault feature extraction. The morphological open-close-median filter is exploited to wrest unique fault features which are then fed as an input to support vector machine to detect and classify the short circuit faults. The acquired graphical and numerical results of the extracted fault features affirm the potency of the offered scheme. The proposed scheme has been verified for different fault cases simulated on high-voltage transmission line modelled using ATP/EMTP with varying system constraints. The performance of the stated technique is also validated for fault detection and classification in real-field transmission lines. The results state that the proposed method is capable of detecting and classifying the faults with adequate precision and reduced computational complexity, in less than quarter of a cycle.

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“…By combining the wavelet transform method with artificial intelligence (AI) methods, the accuracy of fault classification in electrical systems has been improved [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. The discrete wavelet transform (DWT) has been applied previously to decompose fault signals [11,13,14,17].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Artificial Intelligence Methods for Fault Classification of the 115-kV Hybrid Transmission System

Klomjit

Ngaopitakkul

2020

Applied Sciences

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This research proposes a comparison study on different artificial intelligence (AI) methods for classifying faults in hybrid transmission line systems. The 115-kV hybrid transmission line in the Provincial Electricity Authority (PEA-Thailand) system, which is a single circuit single conductor transmission line, is studied. Fault signals in the transmission line were generated by the EMTP/ATPDraw software. Various factors such as fault location, type, and angle were considered. Then, fault signals were analyzed by coefficient details on the first scale of the discrete wavelet transform. Daubechies mother wavelet from MATLAB software was used to decompose the fault signal. The coefficient value of the mother wavelet behaved depending on the position, inception of fault angle, and fault type. AI methods including probabilistic neural networks (PNNs), back-propagation neural networks (BPNNs), and support vector machine (SVM) were used to identify faults. AI input used the maximum first peak coefficients of phase ABC and zero sequence. The results obtained from the study were found to be satisfactory with all AI methodologies having an average accuracy of more than 98% in the case study. However, the SVM technique can provide more accurate results than the PNN and BPNN techniques with less computation burden. Thus, it is suitable for being applied to actual protection systems.

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Fault detection and classification method using DWT and SVM in a power distribution network

Cited by 29 publications

References 8 publications

Mathematical Morphology-Based Feature-Extraction Technique for Detection and Classification of Faults on Power Transmission Line

Mathematical Morphology-Based Feature-Extraction Technique for Detection and Classification of Faults on Power Transmission Line

Combined morphology and SVM-based fault feature extraction technique for detection and classification of transmission line faults

Comparison of Artificial Intelligence Methods for Fault Classification of the 115-kV Hybrid Transmission System

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